Presentations in chronological order:
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
(Other presentations from 2009 and earlier may be made available by request.)
2022
Daniel B. Neill, Mallory Nobles, Ramona Lall, and Robert W. Mathes,
Pre-syndromic surveillance for improved detection of emerging public
health threats. Syndromic Surveillance Symposium (virtual), December
2022. (pdf)
Daniel B. Neill, Boyuan Chen, Yi Wei, and Mallory Nobles, MUSES
Open-Source Software for Pre-Syndromic Disease Surveillance (training
session). Syndromic Surveillance Symposium (virtual), December 2022.
(pdf)
Daniel B. Neill. Machine learning and event detection for urban public
health. Ludwig Maximilian University of Munich (virtual), June 2022, and
Michigan Institute of Technology (virtual), December 2022.
(pdf)
Daniel B. Neill. Use-inspired artificial intelligence and machine learning
for public and population health. University of Texas at Austin, McCombs
School of Business (virtual), April 2022. (pdf)
2021
Daniel B. Neill. Machine learning and event detection for urban public
health. Workshop on Urban Complex Systems (UCS-CCS 2021), Lyon, France
(hybrid), October 2021. (pdf)
Daniel B. Neill. Machine learning for opioid and overdose surveillance.
CMU Symposium on AI and Social Good, May 2021. (pdf)
2020
Daniel B. Neill. Subset scanning for event and pattern detection. IBM
Thomas J. Watson Research Center, Yorktown Heights, NY, February 2020. (pdf)
2019
Mallory Nobles, Ramona Lall, Robert Mathes, and Daniel B. Neill.
Multidimensional semantic scan for pre-syndromic disease surveillance.
International Society for Disease Surveillance Annual Conference, San
Diego, CA, January 2019. Winner of the International Society for
Disease Surveillance Outstanding Student or Post-Degree Abstract Award.
(pdf)
Roberto Souza, Renato Assuncao, Daniel B. Neill, and Wagner Meira Jr. Identifying high-risk areas for dengue infection using mobility patterns on Twitter. International Society for Disease Surveillance
Annual Conference, San Diego, CA, January 2019. (pdf)
Daniel B. Neill. Machine learning, automated algorithms, and risk.
InsurTech Alliance, New York, NY, February 2019. (pdf)
Daniel B. Neill. Subset scanning for event and pattern detection.
Department of Operations Research and Industrial Engineering, Cornell
University, Ithaca, NY, March 2019. (pdf)
Daniel B. Neill. Machine learning and event detection for population
health (invited plenary), Machine Learning for Science and Engineering
Conference, Atlanta, GA, June 2019. (pdf)
2018
Daniel B. Neill. Machine learning for population health and disease
surveillance, 2017-2018. (pdf)
(Presented at: Duke University, Department of Mathematics, November 2018; Machine Learning and Medicine Seminar, Cornell University,
New York, NY, March 2018; New York University, Department of Population Health, January 2017.)
Daniel B. Neill. Subset scanning for event and pattern detection, 2018. (pdf)
(Presented at: University of Connecticut, Department of Statistics, November 2018; invited webinar for IBM Research Africa, August 2018.)
Daniel B. Neill. Novel machine learning methods for public health and
disease surveillance. American Society for Microbiology Biothreats
Conference, Baltimore, MD, February 2018. (pdf)
Daniel B. Neill and Zhe Zhang. Auditing black-box algorithms for fairness
and bias. Workshop on Accountable Decision Systems, New York, NY, February
2018. (pdf)
Daniel B. Neill and Zhe Zhang. Fairness and bias in algorithmic
decision-making. Big Data in Health Symposium, Cornell University, New
York, NY, April 2018. (pdf)
Daniel B. Neill. Machine learning, big data, and development.
International Monetary Fund, Washington, DC, May 2018. (pdf)
Daniel B. Neill. Modeling and detecting patterns in complex urban data. Amazon, New York, NY, July 2018. (pdf)
Daniel B. Neill. Automated algorithms and risk: two sides of the coin. InsurTech Science and Engineering
Innovation Expo, New York, NY, August 2018. (pdf)
Daniel B. Neill. Predictive policing in practice. Workshop on Data-Driven Criminal Justice Reform, New York, NY, October 2018. (pdf)
Daniel B. Neill. New methodological approaches for opioid and overdose surveillance. 3rd Seattle Symposium on Health Care Data Analytics, Seattle, WA, October 2018. (pdf)
Daniel B. Neill. Machine learning for development: challenges, opportunities, and a roadmap. NeurIPS 2018 Workshop on Machine Learning for the Developing World, Montreal, Canada, December 2018.
(pdf)
2017
Dylan Fitzpatrick and Daniel B. Neill. Support vector subset scan for
spatial pattern detection, 2016-2017. (pdf)
(Presented at: GEOMED International Conference on Spatial Statistics,
Spatial Epidemiology, and Spatial Aspects of Public Health, Porto,
Portugal, September 2017; Eighth International Workshop on Applied
Probability, Toronto, Canada, June 2016.)
Sriram Somanchi, Edward McFowland III, and Daniel B. Neill. Detecting
anomalous patterns of care using health insurance claims, 2016-2017. (pdf)
(Presented at: INFORMS Conference on Information Systems and Technology,
Houston, TX, October 2017; INFORMS Workshop on Data Science, Houston, TX,
October 2017; INFORMS Annual Meeting, Nashville, TN, November 2016;
Conference on Digital Experimentation, Cambridge, MA, October 2016;
Workshop on Health IT and Economics, Washington, D.C., October 2016;
Eighth International Workshop on Applied Probability, Toronto, Canada,
June 2016.)
Zhe Zhang and Daniel B. Neill. Identifying significant predictive bias in
classifiers, 2016-2017. (pdf)
(Presented at: Fourth Workshop on Fairness, Accountability, and
Transparency in Machine Learning, Halifax, Canada, August 2017; NIPS
Workshop on Interpretable Machine Learning for Complex Systems, Barcelona,
Spain, December 2016.)
Daniel B. Neill, Chunpai Wang, Feng Chen, and Daniel Hono. Efficient
pattern detection in web-scale graphs by subcore-tree decomposition and
subset scanning. Joint Statistical Meetings, Baltimore, MD, July 2017.
(pdf)
Daniel B. Neill and William Herlands. Machine learning for drug overdose
surveillance. Bloomberg Data for Good Exchange Conference, New York, NY,
September 2017. (pdf)
Daniel B. Neill and Mallory Nobles. A pre-syndromic surveillance approach
for early detection of novel and rare disease outbreaks. Interdisciplinary
Association for Population Health Science Conference, Austin, TX, October
2017. (pdf)
Daniel B. Neill. Multidimensional subset scanning for the public good.
University of Texas at Austin, McCombs School of Business, October 2017.
(pdf)
Daniel B. Neill. Event and pattern detection at the societal scale
(invited keynote). ACM SIGSPATIAL Workshop on Analytics for Local Events
and News, Redondo Beach, CA, November 2017. (pdf)
2016
Daniel B. Neill. Event and pattern detection at the societal scale,
2015-2016. (pdf)
(Presented at: Georgia Institute of Technology, School of Computational
Science and Engineering, March 2016; New York University, Courant
Institute, Department of Computer Science, February 2016; Harvard
University, School of Engineering and Applied Sciences, November 2015;
University of Chicago, Harris School of Public Policy, October
2015.)
Daniel B. Neill. Event and pattern detection for urban systems, 2016. (pdf)
(Presented at: New York University, Wagner School of Public Service, April
2016; New York University, Center for Urban Science and Progress, February
2016.)
Daniel B. Neill. Fast subset scan for population health and disease
surveillance, 2016. (pdf)
(Presented at: Weill Cornell Medical College, Department of Healthcare
Policy and Research, December 2016; Harvard University, Department of
Biostatistics, T.H. Chan School of Public Health, May 2016.)
Edward McFowland III, Sriram Somanchi, and Daniel B. Neill. Efficient
discovery of heterogeneous treatment effects in randomized experiments via
anomalous pattern detection, 2016-2018. (pdf)
(Presented at: Conference on Digital Experimentation, Cambridge, MA,
October 2016; Eighth International Workshop on Applied Probability,
Toronto, Canada, June 2016.)
Feng Chen, Petko Bogdanov, Daniel B. Neill, and Ambuj K. Singh. Anomalous
and significant subgraph detection in attributed networks. Tutorial
presented at IEEE International Conference on Big Data, December
2016. (part
1) (part
2)
2015
Skyler Speakman, Sriram Somanchi, Edward McFowland III, and Daniel B.
Neill. Penalized fast subset scanning. 45th Symposium on the Interface of
Computing Science and Statistics ("Best of JCGS" invited session),
Morgantown, WV, June 2015. (pdf)
Daniel B. Neill and Feng Chen. Human rights event detection from
heterogeneous social media graphs. Human Rights Media Central Workshop,
Pittsburgh, PA, July 2015. (pdf)
Seth Flaxman, Andrew Gelman, Andrew Gordon Wilson, Daniel B. Neill, Hannes
Nickisch, Alex Smola, and Aki Vehtari. Large-scale Gaussian processes for
spatiotemporal modeling of disease incidence. Joint Statistical Meetings,
Seattle, WA, August 2015. (pdf)
Jason Hong, Tom Mitchell, Daniel B. Neill, and Aarti Singh. Machine
learning and health: from neurons to society. World Economic Forum: Annual
Meeting of the New Champions, Dalian, China, September 2015. (pdf)
Seth R. Flaxman, Andrew Gordon Wilson, Daniel B. Neill, Hannes Nickisch,
and Alexander J. Smola. Novel approaches to local area spatiotemporal
crime rate forecasting with Gaussian processes. American Society of
Criminology Annual Meeting, Washington, DC, November 2015. (pdf)
2014
Daniel B. Neill. Scaling up event and pattern detection to big data. MIT
Workshop on Challenges in Big Data for Data Mining, Machine Learning and
Statistics, Cambridge, MA, March 2014. (pdf)
Daniel B. Neill. Scaling up event and pattern detection to big data. NYU
Stern School of Business, Information Systems Seminar, New York, NY, April
2014. (pdf)
Feng Chen and Daniel B. Neill. Non-parametric scan statistics for event
detection and forecasting in heterogeneous social media graphs. Seventh
International Workshop on Applied Probability, Antalya, Turkey, June 2014.
(pdf)
Sriram Somanchi and Daniel B. Neill. A star-shaped scan statistic for
detecting irregularly-shaped spatial clusters. Seventh International
Workshop on Applied Probability, Antalya, Turkey, June 2014. (pdf)
Edward McFowland III and Daniel B. Neill. Discovering novel anomalous
patterns in general data. Statistical Learning and Data Mining Meeting on
Data Mining in Business and Industry, Durham, NC, June 2014. (pdf)
Seth Flaxman, Alex Smola, and Daniel B. Neill. Kernel space-time
interaction tests for identifying leading indicators of crime. Joint
Statistical Meetings, Boston, MA, August 2014. (pdf)
Mallory Nobles, Seth Flaxman, and Daniel B. Neill. Urban predictive
analytics. INFORMS Annual Meeting, San Francisco, CA, November 2014. (pdf)
Sriram Somanchi, David Choi, and Daniel B. Neill. StarScan: a novel scan
statistic for irregularly-shaped spatial clusters. International Society
for Disease Surveillance Annual Conference, Philadelphia, PA, December
2014. (pdf)
Mallory Nobles, Lana Deyneka, Amy Ising, and Daniel B. Neill. Identifying
emerging novel outbreaks in textual emergency department data.
International Society for Disease Surveillance Annual Conference,
Philadephia, PA, December 2014. (pdf)
2013
Daniel B. Neill. Fast subset scanning for scalable event and pattern
detection. Stony Brook University, Stony Brook, NY, May 2013. (pdf)
Seth Flaxman and Daniel B. Neill. New tests for space-time interaction in
spatio-temporal point processes. 2nd Spatial Statistics Conference,
Columbus, OH, June 2013. (pdf)
Daniel B. Neill. Machine learning and event detection for the public good.
Data Science for the Social Good Summer Fellowship Program, Chicago, IL,
July 2013. (pdf)
Feng Chen and Daniel B. Neill. Non-parametric scan statistics for event
detection and forecasting in heterogeneous social media graphs. INFORMS
Annual Meeting, Minneapolis, MN, October 2013. (pdf)
Feng Chen and Daniel B. Neill. Non-parametric scan statistics for disease
outbreak detection on Twitter. International Society for Disease
Surveillance Annual Conference, New Orleans, LA, December 2013. (pdf)
Skyler Speakman, Sriram Somanchi, Edward McFowland III, and Daniel B.
Neill. Penalized fast subset scanning. 6th International Conference on
Computational and Methodological Statistics, London, UK, December 2013.
(pdf)
2012
Daniel B. Neill. Analytical methods for large scale surveillance of
unstructured data. International Conference on Digital Disease Detection,
Boston, MA, February 2012. (pdf)
Edward McFowland III, Skyler Speakman, and Daniel B. Neill. Fast
generalized subset scan for anomalous pattern detection. Sixth
International Workshop on Applied Probability, Jerusalem, Israel, June
2012. (pdf)
Daniel B. Neill, Skyler Speakman, Edward McFowland III, and Sriram
Somanchi. Efficient subset scanning with soft constraints. Sixth
International Workshop on Applied Probability, Jerusalem, Israel, June
2012. (pdf)
Skyler Speakman, Edward McFowland III, and Daniel B. Neill. Scalable
detection of anomalous patterns with connectivity constraints. 29th
Quality and Productivity Research Conference, Long Beach, CA, June 2012.
(pdf)
Daniel B. Neill and Seth Flaxman. Detecting spatially localized subsets
of leading indicators for event prediction. 32nd International Symposium
on Forecasting, Boston, MA, June 2012. (pdf)
Daniel B. Neill. Predicting and preventing emerging outbreaks of crime.
CMU Workshop on Machine Learning and Social Sciences, Pittsburgh, PA,
October 2012. (pdf)
Sriram Somanchi and Daniel B. Neill. Fast graph structure learning from
unlabeled data for event detection. INFORMS Annual Conference, Phoenix,
AZ, October 2012.
Skyler Speakman, Yating Zhang, and Daniel B. Neill. Tracking dynamic
water-borne outbreaks with temporal consistency constraints.
International Society for Disease Surveillance Annual Conference, San
Diego, CA, December 2012. (pdf)
Daniel B. Neill and Tarun Kumar. Fast multidimensional subset scan for
outbreak detection and characterization. International Society for Disease
Surveillance Annual Conference, San Diego, CA, December 2012. (pdf)
2011
Daniel B. Neill. Spatial scan tips and tricks for practical outbreak
detection. Invited webinar for the International Society for Disease
Surveillance, January 2011. (pdf)
Daniel B. Neill. Spatial and subset scanning for multivariate health
surveillance. Data Fusion Research Meeting, Ottawa, ON, March 2011. (pdf)
Daniel B. Neill. Machine learning for population health and disease
surveillance. Advanced Analytics Workshop, Washington, DC, April 2011. (pdf)
Edward McFowland III and Daniel B. Neill. Fast generalized subset scan
for anomalous pattern detection in mixed data sets. 17th Conference for
African-American Researchers in the Mathematical Sciences, Los Angeles,
CA, June 2011.
Daniel B. Neill. Fast multivariate subset scanning for scalable cluster
detection. Joint Statistical Meetings 2011, Miami, FL, August 2011. (pdf)
Edward McFowland III and Daniel B. Neill. Efficient methods for anomalous
pattern detection in general datasets. INFORMS Annual Conference,
Charlotte, NC, November 2011. (pdf)
Sriram Somanchi and Daniel B. Neill. Fast learning of graph structure from
unlabeled data for anomalous pattern detection. INFORMS Annual Conference,
Charlotte, NC, November 2011. (pdf)
Skyler Speakman and Daniel B. Neill. Dynamic pattern detection with
connectivity and temporal consistency constraints. INFORMS Annual
Conference, Charlotte, NC, November 2011. (pdf)
Kan Shao, Yandong Liu, and Daniel B. Neill. A generalized fast subset sums
framework for Bayesian event detection. Presented at the 11th IEEE
International Conference on Data Mining, 2011. (pdf)
2010
Daniel B. Neill, Fast subset scanning for multivariate event detection.
ENAR 2010 Annual Meeting, New Orleans, LA, March 2010. (pdf)
Edward McFowland III, Skyler Speakman, and Daniel B. Neill. Fast
generalized subset scan for anomalous pattern detection. Sixteenth
Conference for African American Researchers in the Mathematical Sciences,
Baltimore, MD, June 2010. (pdf)
Daniel B. Neill. Fast subset sums for scalable Bayesian detection and
visualization. Fifth International Workshop on Applied Probability,
Madrid, Spain, July 2010. (pdf)
Skyler Speakman, Edward McFowland III, and Daniel B. Neill. Scalable
detection of anomalous patterns with connectivity constraints. INFORMS
Annual Conference, Austin, TX, November 2010. (pdf)
Edward McFowland III, Skyler Speakman, and Daniel B. Neill. Fast
generalized subset scan for anomalous pattern detection. INFORMS Annual
Conference, Austin, TX, November 2010. (pdf)
Daniel B. Neill, Edward McFowland III, and Huanian Zheng. Fast subset
scan for multivariate spatial biosurveillance. International Society for
Disease Surveillance Annual Conference, Park City, UT, December 2010. (pdf)
Daniel B. Neill and Yandong Liu. Generalized fast subset sums for
Bayesian detection and visualization. International Society for Disease
Surveillance Annual Conference, Park City, UT, December 2010. (pdf)
Daniel B. Neill. Research challenges for biosurveillance: the next ten
years (invited plenary). International Society for Disease Surveillance
Annual Conference, Park City, UT, December 2010. (pdf)
2009
Daniel B. Neill and Weng-Keen Wong. A tutorial on event detection.
Presented at the 15th ACM SIGKDD Conference on Knowledge Discovery and
Data Mining, 2009. (pdf)
Daniel B. Neill. Fast subset sums for multivariate Bayesian scan
statistics. International Society for Disease Surveillance Annual
Conference, Miami, FL, December 2009. (pdf)
Skyler Speakman and Daniel B. Neill. Fast graph scan for scalable
detection of arbitrary connected clusters. International Society for
Disease Surveillance Annual Conference, Miami, FL, December 2009. (pdf)
I gratefully acknowledge funding support from the National Science Foundation, grants IIS-1926470, IIS-0916345, IIS-0911032,
and IIS-0953330, the NSF Program on Fairness in Artificial Intelligence in Collaboration with Amazon, grant IIS-2040898, a
UPMC Healthcare Technology Innovation Grant, funding from the John D. and Catherine T. MacArthur Foundation and Richard King
Mellon Foundation, and a gift from the Disruptive Health Technology Institute. Any opinions, findings, and conclusions or
recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the
National Science Foundation, UPMC, DHTI, Amazon, Richard King Mellon Foundation, or MacArthur Foundation.
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Last updated: 11/20/2022